Trend analysis of rainfall time series for Sindh river basin in India

The main goal of this paper is to estimate a set of optimal seasonal, daily, and hourly values of atmospheric turbidity and surface radiative parameters Ångström’s turbidity coefficient (β), Ångström’s wavelength exponent (α), aerosol single scattering albedo (ω_o), forward scatterance (F_c) and average surface albedo (ρ_g), using the Brute Force multidimensional minimization method to minimize the difference between measured and simulated solar irradiance components, expressed as cost functions. In order to simulate the components of short-wave solar irradiance (direct, diffuse and global) for clear sky conditions, incidents on a horizontal surface in the Metropolitan Area of Rio de Janeiro (MARJ), Brazil (22° 51′ 27″ S, 43° 13′ 58″ W), we use two parameterized broadband solar irradiance models, called CPCR2 and Iqbal C, based on synoptic information. The meteorological variables such as precipitable water (u_w) and ozone concentration (u_o) required by the broadband solar models were obtained from moderate-resolution imaging spectroradiometer (MODIS) sensor on Terra and Aqua NASA platforms. For the implementation and validation processes, we use global and diffuse solar irradiance data measured by the radiometric platform of LabMiM, located in the north area of the MARJ. The data were measured between the years 2010 and 2012 at 1-min intervals. The performance of solar irradiance models using optimal parameters was evaluated with several quantitative statistical indicators and a subset of measured solar irradiance data. Some daily results for Ångström’s wavelength exponent α were compared with Ångström’s parameter (440–870 nm) values obtained by aerosol robotic network (AERONET) for 11 days, showing an acceptable level of agreement. Results for Ångström’s turbidity coefficient β, associated with the amount of aerosols in the atmosphere, show a seasonal pattern according with increased precipitation during summer months (December–February) in the MARJ.     

Trend analysis of precipitation in Jharkhand State,India

Jharkhand is one of the eastern states of India which has an agriculture-based economy. Uncertain and erratic distribution of precipitation as well as a lack of state water resources planning is the major limitation to crop growth in the region. In this study, the spatial and temporal variability in precipitation in the state was examined using a monthly precipitation time series of 111 years (1901–2011) from 18 meteorological stations. Autocorrelation and Mann–Kendall/modified Mann–Kendall tests were utilized to detect possible trends, and the Theil and Sen slope estimator test was used to determine the magnitude of change over the entire time series. The most probable change year (change point) was detected using the Pettitt–Mann–Whitney test, and the entire time series was sub-divided into two parts: before and after the change point. Arc-Map 9.3 software was utilized to assess the spatial patterns of the trends over the entire state. Annual precipitation exhibited a decreasing trend in 5 out of 18 stations during the whole period. For annual, monsoon and winter periods of precipitation, the slope test indicated a decreasing trend for all stations during 1901–2011. The highest variability was observed in post-monsoon precipitation (77.87 %) and the lowest variability was observed in the annual series (15.76 %) over the 111 years. An increasing trend in precipitation in the state was found during the period 1901–1949, which was reversed during the subsequent period (1950–2011).     

Characteristics of ambient ammonia over Delhi,India

In the present paper, we have characterized the ambient ammonia over Delhi along with other trace gases (NH₃, NO, NO₂, SO₂ and CO) and particulates (PM_2.5 and PM₁₀) measured during December 2011 to June 2012. The average mixing ratios of ambient NH₃, NO, NO₂, SO₂ and CO were recorded as 21.2 ± 5.4, 19.5 ± 4.9, 17.4 ± 1.4, 1.7 ± 0.5 ppb and 1.6 ± 0.7 ppm, respectively, during winter, whereas the average mixing ratios of ambient NH₃, NO, NO₂, SO₂ and CO were recorded as 20.8 ± 4.7, 21.7 ± 6.3, 16.8 ± 3.1, 2.2 ± 0.8 ppb and 1.8 ± 0.9 ppm, respectively, during summer. In the present case, non-significant seasonal and diurnal variations of NH₃, NO, NO₂, SO₂ and CO were observed during both the seasons. The average monthly NH₃/NH₄ ⁺ ratios varied from 0.28 to 2.56 with an average value of 1.46 in winter. The higher NH₃/NH₄ ⁺ ratio (3.5) observed in summer indicates the abundance of NH₃ in the atmosphere during summer. The higher fraction of particulate NH₄ ⁺ observed in winter than summer attributes to the conversion of gaseous NH₃ into NH₄ ⁺. The results emphasized that the traffic could be one of the significant sources of ambient NH₃ at the urban site of Delhi as illustrated by positive correlations of NH₃ with traffic-related pollutants (NO, NO₂ and CO). Surface wind analysis and wind directions also support the roadside traffic and agricultural activities at the nearby area indicating possible major sources of ambient NH₃ at the study site.     

Trend and homogeneity analysis in temperature and rainfall series in western Black Sea region,Turkey

Theoretical and Applied Climatology - This paper includes a study which applied homogeneity and trend tests on time series of monthly mean temperature and monthly total rainfall recorded in the...     

Wavelet-based combination approach for modeling sub-divisional rainfall in India

Agriculture in India is highly sensitive to climatic variations particularly to rainfall and temperature; therefore, any change in rainfall and temperature will influence crop yields. An understanding of the spatial and temporal distribution and changing patterns in climatic variables is important for planning and management of natural resources. Time series analysis of climate data can be a very valuable tool to investigate its variability pattern and, maybe, even to predict short- and long-term changes in the series. In this study, the sub-divisional rainfall data of India during the period 1871 to 2016 has been investigated. One of the widely used powerful nonparametric techniques namely wavelet analysis was used to decompose and de-noise the series into time–frequency component in order to study the local as well as global variation over different scales and time epochs. On the decomposed series, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were applied and by means of inverse wavelet transform, the prediction of rainfall for different sub-divisions was obtained. To this end, empirical comparison was carried out toward forecast performance of the approaches namely Wavelet-ANN, Wavelet-ARIMA, and ARIMA. It is reported that Wavelet-ANN and Wavelet-ARIMA approach outperforms the usual ARIMA model for forecasting of rainfall for the data under consideration.     

Extreme monsoonal rainfall deficits in India

Summary Data from 306 stations in India, for the 70 years (1901–70) of summer (June–September) monsoonal rainfall, are grouped into 32 sub-regions. Extreme event theory is used to analyse the return periods of extreme rainfall deficits within each of these sub-regions, using the log-Pearson type III frequency distribution in a spatial rather than a temporal context. The resultant estimates for 2, 5 and 10 year return periods are compared with the patterns derived from the Gaussian frequency distribution applied to the 306 stations individually; the 50 and 100 year return period estimates are also considered.

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Zusammenfassung Die Daten über den sommerlichen Monsunregen (Juni–September) aus 70 Jahren (1901–70) von 306 Stationen in Indien werden in 32 Teilgebiete gruppiert. Zur Analyse der wiederkehrenden Perioden extremen Regendefizits in jedem dieser Teilgebiete wird die Theorie extremer Ereignisse verwendet, wobei die log-Pearson-Häufigkeitsverteilung vom Typ III eher in einem räumlichen als zeitlichen Zusammenhang verwendet wird. Die sich daraus ergebenden Schätzungen für wiederkehrende Perioden von 2, 5 und 10 Jahren werden mit den Mustern verglichen, die mit Hilfe der auf alle 306 Stationen einzeln angewandten Gaußschen Häufigkeitsverteilung gewonnen wurden. Die Abschätzung 50jähriger und 100jähriger Ereignisse wird ebenfalls besprochen.

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With 6 Figures     

Deforestation-induced costs on the drinking water supplies of the Mumbai metropolitan,India

The success of incorporating natural capital into resource- and land-use decisions hinges on the ability to quantify the ecosystem services, forecast the returns to the investments, convert these values into effective policy and finance mechanisms, and the presence of well-functioning institutions and infrastructure. However, ecosystem production functions i.e., the relationship between regulatory functions of the ecosystem and the economic activity it protects or supports are often poorly understood. Even with respect to Forest Watershed Services – a service that is widely recognized and even institutionalized through market based mechanisms in some parts of the world – the biophysical relationships between forests and services such as stream flow stabilization, water quality and water quantity are undefined, particularly for the tropics. For this reason, this study through time series data and multivariate analysis characterizes the relationships between Forest Cover (all lands with tree cover of a canopy density of 10% and above when projected vertically on the horizontal ground with minimum areal extent of 1 ha), water quality and cost of water treatment in the Western Ghats of peninsular India. In particular, the recursive relationship between the economic and environmental components is estimated by tracing the effects through the two-stage model. Annual value of impacts (increased ‘treatment cost’, increased ‘water losses due to backwash and desludging’, and changes in ‘water yield’) induced by loss of Forest Cover is estimated as 64.96 Indian rupee/m³ treated water/ha/year ($1.32/m³ treated water/ha/year). At an annual rate of change in the forest cover by −0.0088% (average annual rate of change in the forest cover between the years 1994–2007) the deforestation induced costs translate to 3.73 million Indian rupee/year ($0.075 million/year) according to the 2010–2011 prices for the Panjrapur treatment plant of the Municipal Corporation of Greater Mumbai. Thus, if deforestation is avoided the Municipal Corporation can save significant amount towards recurring costs of water treatment and to some extent mitigate the costs for the development of a new source.     

Public perceptions of rainfall change in India

People’s perceptions of changes in local weather patterns are an important precursor to proactive adaptation to climate change. In this paper, we consider public perceptions of changes in average rainfall in India, analyzing the relationship between perceptions and the instrumental record. Using data from a national sample survey, we find that local instrumental records of precipitation are a strong predictor of perceived declines in rainfall. Perceptions of decreasing rainfall were also associated with perceptions of changes in extreme weather events, such as decreasing frequency of floods and severe storms, increasing frequency of droughts, and decreasing predictability of the monsoon. Higher social vulnerability—including low perceived adaptive capacity and greater food and livelihood dependence on local weather—was also associated with perceptions of decreasing rainfall. While both urban and rural respondents were likely to perceive local changes in precipitation, we show that rural respondents in general were more sensitive to actual changes in precipitation. Individual perceptions of changes in local climate may play an important role in shaping vulnerability to global climate change, adaptive behavior, and support for adaptation and mitigation policies. Awareness of local climate change is therefore particularly important in regions where much of the population is highly exposed and sensitive to the impacts of climate change.     

Statistical analysis of annual maximum rainfall in North-East India: an application of LH-moments

An attempt has been made to determine the best fitting distribution to describe the annual series of maximum daily rainfall data for the period 1966 to 2007 of nine distantly located stations in North East India. The LH-moments of order zero (L) to order four (L₄) are used to estimate the parameters of three extreme value distributions viz. generalized extreme value distribution (GEV), generalized logistic distribution (GLD), and generalized Pareto distribution (GPD). The performances of the distributions are assessed by evaluating the relative bias (RBIAS) and relative root mean square error (RRMSE) of quantile estimates through Monte Carlo simulations. Then, the boxplot is used to show the location of the median and the associated dispersion of the data. Finally, it can be revealed from the results of boxplots that zero level of LH-moments of the generalized Pareto distribution would be appropriate to the majority of the stations for describing the annual maximum rainfall series in North East India.     

Mixed-layer characteristics as related to the monsoon climate of New Delhi,India

Annual variations of mixed-layer characteristics at New Delhi, India have been studied for a weak monsoon (1987) and a strong monsoon (1988) year. In the weak monsoon year (1987), the maximum mixing depthh _max was found to have a value of around 3000 m during the pre-monsoon, less than 2000 m during the summer monsoon, around 2000 m during the post-monsoon, and less than 1000 m in the winter season. For the strong monsoon year (1988),h _max values were less than 1987 values for comparable periods throughout the year. The seasonal and yearly differences ofh _max were explained by the surface energy balance and potential temperature gradient at a time close to sunrise. According to the spatial patterns of obtained by an objective analysis of the 850 to 700 hPa layers. mixed-layer characteristics obtained at New Delhi are representative of the north and central regions of India.     

Surface pressure and summer monsoon rainfall over India

The relationship between the all-India summer monsoon rainfall and surface pressure over the Indian region has been examined to obtain a useful predictor for the monsoon rainfall. The data series of all-India monsoon rainfall and the mean pressures of three seasons before and after the monsoon season as well as the winter-to-spring pressure tendency (MAM-DJF) at 100 stations for the period 1951-1980 have been used in the analysis. The all-India monsoon rainfall is negatively correlated with the pressure of the spring (MAM) season preceding the monsoon and winter-to-spring seasonal difference as pressure tendency (MAM-DJF), at almost all the stations in India, and significantly with the pressures over central and northwestern regions. The average mean sea level pressure of six stations (Jodhpur, Ahmedabed, Bombay, Indore, Sagar and Akola) in the Western Central Indian (WCI) region showed highly significant (at 1% level) and consistent CCs of -0.63 for MAM and -0.56 for MAM-DJF for the period 1951–1980. Thus, the pre-monsoon seasonal pressure anomalies over WCI could provide a useful parameter for the long-range forecasting scheme of the Indian monsoon rainfall.     

Probabilities and distribution of monsoon rainfall in normal, flood and drought years over India

Summary The paper deals with the variability of summer-monsoon rainfall during normal, flood and drought years over India. During flood years the monsoon rainfall increases mostly all over parts of the country and large area less than 100 cm isohytel covers Orissa and adjoining Madhya Pradesh. During drought years the rainfall amount decreases over the entire country and isohytel of 100 cm shrinks to almost a point. The variability of monsoon rainfall from flood to normal to drought years depends upon the number of depression/low-pressure area which form over the North Bay and move inland. To understand the intraseasonal and interannual variability of the monsoon rainfall, daily and seasonal anomalies has been performed by using the Empirical Orthogonal Function analysis. Further Empirical Orthogonal Function (EOF) analysis is carried out on these data to find out the nature of rainfall distribution in different monsoon categories namely normal, flood and drought years. This technique thus serves to identify spatial and temporal patterns characteristics of possible physical significance. Received July 25, 2000/Revised September 26, 2000     

A universal spectrum for interannual variability of monsoon rainfall over India

Continuous periodogram analyses of 115 years (1871-1985) summer monsoon rainfall over the Indian region show that the power spectra follow the universal and unique inverse power law form of the statistical normal distribution with the percentage contribution to total variance representing the eddy probability corresponding to the normalized standard deviation equal to [(log L/log T50) – 1] where L is the period length in years and T50 the period up to which the cumulative percentage contribution to total variance is equal to 50. The above results are con-sistent with a recently developed non-deterministic cell dynamical model for atmospheric flows. The implications of the above result for prediction of interannual variability of rainfall is discussed.     

Trends and fluctuations of rainfall regime in the Brahmaputra and Barak basins of Assam, India

Severe weather conditions create negative impacts on humans. One of the severe weather conditions is storms. In the scope of this study, the storm effects on the Marmara Region were investigated using data for the period 2000 to 2010 giving the hourly averaged wind direction and speed in the Marmara Region, as provided by the Turkish Meteorological Service. The monthly distribution of storms has been estimated and their daily variability has been investigated. Additionally, air masses that cause storms have been determined by using NCEP/NCAR re-analysis data. Other than this, by studying the storms, extreme values have been ascertained and northerly values from these storm values (since in percentage terms they are larger) and hourly variability in a single day for 17 weather stations have been depicted graphically. The days that northerly storms reached extreme values are shown with the meteorological maps (surface chart, 850, 700, 500, and 300 hPa) and the temperature diagrams for Istanbul in the Marmara Region have been examined and analysed.     

A 694-year tree-ring based rainfall reconstruction from Himachal Pradesh, India

We developed ring-width chronologies of Cedrus deodara [(Roxb.) G. Don] and Pinus gerardiana (Wall. Ex. Lamb) from a homogeneous moisture stressed area in Kinnaur, Himachal Pradesh. Running correlation using a 50-year window with overlap of 25 years showed strong correlations between these species chronologies during the entire common period (ad 1310–2005). Response function analysis indicated that except for January–February, precipitation has a direct relationship with growth of these species. We therefore combined both the species chronologies to develop a statistically calibrated reconstruction of March–July precipitation that spans from ad 1310–2004, and explains 46% of the variance contained in the instrumental data from the calibration period 1951–1994. In the past 694 years of the reconstruction, the wettest period was in the twentieth century (1963–1992) and the driest in the eighteenth century (1773–1802). The relationships observed between reconstructed precipitation and Indian summer monsoon on interdecadal scale, SOI, PDO and NAO indicate the potential utility of such long-term reconstructions in understanding the large-scale climate variability. Multi-taper method (MTM) spectral analysis indicated significant (p < 0.05) spectral peaks at 2–4, 6, 8, 10, 30, 33, 37 and 40–42 years in the reconstructed precipitation data.     

Groundwater depletion limits the scope for adaptation to increased rainfall variability in India

Recent studies have found that increasing intra-seasonal precipitation variability will lead to substantial reductions in rice production in India by 2050, independently of the effect of rising temperatures. However, these projections do not account for the possibility of adaptations, of which the expansion of irrigation is the primary candidate. Using historical data on irrigation, rice yields, and precipitation, I show that irrigated locations experience much lower damages from increasing precipitation variability, suggesting that the expansion of irrigation could protect Indian agriculture from this future threat. However, accounting for physical water availability shows that under current irrigation practices, sustainable use of irrigation water can mitigate less than a tenth of the climate change impact. Moreover, if India continues to deplete its groundwater resources, the impacts of increased variability are likely to increase by half.